Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas - Gómez Echavarría, Alejandro (Ingenierias, 2018)
This research project assesses and proposes different strategies for noise reduction based on the undecimated discrete wavelet transform to process noisy bioacoustic signals. The proposed strategies are based on different criteria for estimating the level of noise present in the signal to perform a soft thresholding on the deatail coefficients obtained from the wavelet decomposition. The proposed algorithms, called LAstd, LDstd and LDmad, use the standard deviation of the last approximation, the standard deviation of each detail at each level and the median absolute deviation to estimate the noise level respectively. The strategies are compared with Stein's unbiased risk threshold estimation method (SURE) implemented with the Matlab function wden from the wavelet toolbox which uses the discrete wavelet transform. Three sets of data were used to assess the algorithms. In a first approach, the strategies were evaluated using different owl calls with different additive noise profiles and amplitudes. To test the tolerance of the algorithms to ambient noise, a data set of fragments with acoustic events of high biological activity were extracted from field recordings. Finally, the algorithms were tested with the full recordings of the Colombian ecosystems which contained the acoustic events. The results were quantified using the signal-to-noise ratio and the spectral entropy. To complement the results, a visual analysis of the spectrograms of the processed signals was made. The methodology LAstd obtained an excellent performance when processing the owl calls contaminated with white Gaussian noise, however it is not tolerant to colored noise and narrow band noise. On the other hand, the methodologies LDstd and LDmad show a better performance in signals with colored noise, demonstrating their ability to process field recordings with ambient noise. The implementation of a smoothing parameter “q” for the thresholding in the proposed methodologies allowed to adjust the processing to avoid the loss of important information, giving to the algorithms versatility to perform well in different scenarios